A Learning Methodology In Uncertain And Imprecise Environments
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Departamento de Ciencias de la Computaci'on e Inteligencia Artificial
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A step by step methodology for learning fuzzy rules is presented. This methodology tries to be general enough to give a framework within which different learning methods in an environment of uncertainty and imprecision could be developed. The final product will always be an uncertainty distribution on the different rules representing the behaviour of the system. The particular uncertainty distribution depends on the concrete functions selected in each step of the process. The learning approach can also be considered as a way to construct uncertainty measures from data. Keywords: Machine learning; Fuzzy domains; Uncertainty models; System identification; Fuzzy rules 1 Introduction Inductive learning approaches try to structure complex data in order to describe the behaviour of a system. The objective of this work is to propose a general framework within which different learning methods in an environment of uncertainty and imprecision could be developed. In order to suggest a methodolog...